The promises and potential of assisted driving and autonomous driving technologies rely on an ability to quickly and accurately classify, detect, and/or locate oncoming and/or surrounding objects. Various technologies that have been brought to bear to provide quick and accurate classification, detection, and/or location information include Radio Detection And Ranging (RADAR) subsystems and Light Detection And Ranging (LIDAR) subsystems, Sound Navigation and Ranging (SONAR) subsystems, and image analysis techniques. With respect to image analysis techniques, a camera mounted on a vehicle may capture images of oncoming and/or surrounding road and/or environment scenes for digital processing.
Over the years, improvements in the techniques applied for image analysis, such as the use of Convolutional Neural Networks (CNNs), have made impressive gains in the accuracy of image analysis. However, techniques, such as the use of CNNs, can be computationally intense both to develop and to deploy, raising problems for the requirements to quickly and accurately provide classification, detection, and/or location information and/or to flexibly and responsively improve analysis techniques. Innovations able to maintain gains in accuracy while successfully managing computation needs could be capitalized upon to improve analyses development. Additionally, such innovations could better bring gains in accuracy to assisted and autonomous driving for purposes of providing detection, classification, and/or location information and/or providing redundancy to and/or filling in gaps in similar information provided by other technologies.